Quantum GA-driven Digital Twin for task urgency-aware partitioning and offloading in multi UAV-Aided MEC systems

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) empowers smart mobile devices (SMDs) to efficiently handle computation- and resource-intensive applications, particularly in critical scenarios. The integration of Digital Twin (DT) technology enhances scalability and streamlines the management of multi-user, multi-UAV-assisted MEC systems. This research focuses on partial task offloading within DT-enabled UAV-assisted MEC, addressing the joint problem of task partitioning and offloading using a quantum-inspired genetic algorithm (QIGA). The quantum chromosome is encoded and decoded through linear hashing. Task partitioning is performed to optimize system efficiency in terms of energy, latency, and load distribution across the MEC, while also considering task urgency. The fitness function incorporates two penalty factors to eliminate solutions that violate task deadlines or exceed the energy constraints of SMDs and edge servers. The QIGA is demonstrated to operate in polynomial time across all phases. Extensive simulations under various scenarios reveal that the proposed QIGA outperforms other algorithms in terms of energy efficiency, delay reduction, and load balancing within UAV-assisted MEC. Statistical analyses further validate the reliability and effectiveness of the results.